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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Comparison of free-piston Stirling engine model predictions with RE1000 engine test data

Predictions of a free-piston Stirling engine model are compared with RE1000 engine test data taken at NASA-Lewis Research Center. The model validation and the engine testing are being done under a joint interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA-Lewis. A kinematic code developed at Lewis was upgraded to permit simulation of free-piston engine performance; it was further upgraded and modified at Lewis and is currently being validated. The model predicts engine performance by numerical integration of equations for each control volume in the working space. Piston motions are determined by numerical integration of the force balance on each piston or can be specified as Fourier series. In addition, the model Fourier analyzes the various piston forces to permit the construction of phasor force diagrams. The paper compares predicted and experimental values of power and efficiency and shows phasor force diagrams for the RE1000 engine displacer and piston. Further development plans for the model are also discussed.

Tew, R. C.↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
DOE OSTI2023

Learning local equivariant representations for large-scale atomistic dynamics

Abstract A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1992

Mechanical design of a rotary balance system for NASA. Langley Research Center's vertical spin tunnel

A new, lightweight Rotary Balance System is presently being fabricated and installed as part of a major upgrade to the existing 20-Foot Vertical Spin Tunnel (Figure 1) located at NASA's Langley Research Center. This upgrade, to improve model testing productivity of the only free-spinning vertical wind tunnel in the Western Hemisphere, includes a modern fan/drive and tunnel control system, an updated video recording system, and the new rotary balance system. The rotary balance is a mechanical apparatus which enables the measurement of aerodynamic force and moment data under spinning conditions (100 rpm). This data is used in spin analysis and is vital to the implementation of large amplitude maneuvering simulations required for all new high performance aircraft. The present system is over 40 years old and is worn beyond acceptable tolerances, as well as being susceptible to frequent breakdowns. It has serious limitations in model weight, position adjustment and spin-rate capabilities. The new rotary balance system described in this report will permit greater test efficiency and improved data accuracy. Rotary Balance testing with the model enclosed in a tare bag can also be performed to obtain resulting model forces from the spinning operation. The rotary balance system will be stored against the tunnel sidewall during free-flight model testing.

J. W. Allred↗
NASA NTRS1995

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Simon, William E.↗
NASA NTRS2000

Trajectory Orientation: A Technology-Enabled Concept Requiring a Shift in Controller Roles and Responsibilities

The development of a decision support tool (DST) for the en-route domain with accurate conflict prediction time horizons of 20 minutes has introduced an interesting problem. A 20 minute time horizon for conflict prediction often results in the predicted conflict occurring one or more sectors downstream from the sector controller who "owns" (i-e., is responsible for the safe separation of aircraft) one or both of the aircraft in the conflict pair. Based on current roles and responsibilities of today's en route controllers, the upstream controller would not resolve this conflict. In most cases, the downstream controller would wait until the conflicting aircraft entered higher sector before resolving the conflict. This results in a delay of several minutes from the time when the conflict was initially predicted. This delay is inefficient from both a controller workload and user's cost of operations perspective. Trajectory orientation, a new concept for facilitating an efficient, conflict-free flight path across several sectors while conforming to metering or miles-in-trail spacing, is proposed as an alternative to today's sector-oriented method. This concept necessitates a fundamental shift in thinking about inter-sector coordination. Instead of operating independently, with the main focus on protecting their internal airspace, controllers would work cooperatively, depending on each other for well-planned, conflict-free flow of aircraft. To support the trajectory orientation concept, a long time horizon (15 to 20 minutes) for conflict prediction and resolution would most likely be a primary requirement. In addition, new tools, such as controller-pilot data link will be identified to determine their necessity and applicability for trajectory orientation. Finally, with significant controller participation from selected Air Route Traffic Control Centers, potential shifts in R-side/D-side roles and responsibilities as well as the creation of a new controller position for multi-sector planning will be examined to determine the most viable solutions.

Leiden, Ken↗
NASA NTRS2004

Nickel-Hydrogen and Lithium Ion Space Batteries

The tasks of the Electrochemistry Branch of NASA Glenn Research Center are to improve and develop high energy density and rechargeable, life-long batteries. It is with these batteries that people across the globe are able to power their cell phones, laptop computers, and cameras. Here, at NASA Glenn Research Center, the engineers and scientists of the Electrochemistry branch are leading the way in the development of more powerful, long life batteries that can be used to power space shuttles and satellites. As of now, the cutting edge research and development is being done on nickel-hydrogen batteries and lithium ion batteries. Presently, nickel-hydrogen batteries are common types of batteries that are used to power satellites, space stations, and space shuttles, while lithium batteries are mainly used to power smaller appliances such as portable computers and phones. However, the Electrochemistry Branch at NASA Glenn Research Center is focusing more on the development of lithium ion batteries for deep space use. Because of the limitless possibilities, lithium ion batteries can revolutionize the space industry for the better. When compared to nickel-hydrogen batteries, lithium ion batteries possess more advantages than its counterpart. Lithium ion batteries are much smaller than nickel-hydrogen batteries and also put out more power. They are more energy efficient and operate with much more power at a reduced weight than its counterpart. Lithium ion cells are also cheaper to make, possess flexibility that allow for different design modifications. With those statistics in hand, the Electrochemistry Branch of NASA Glenn has decided to shut down its Nickel-Hydrogen testing for lithium ion battery development. Also, the blackout in the summer of 2003 eliminated vital test data, which played a part in shutting down the program. from the nickel-hydrogen batteries and compare it to past data. My other responsibilities include superheating the electrolyte that is used in the nickel-hydrogen cell in a calorimeter to test its performance under various conditions. 1 used a program called Arbin to study my data. The Arbin allows me to look at different parameters such as pressure and time and how they affect the changing temperature of the electrolyte that is being tested. In addition, I had the responsibility of taking apart and modifying battery coolers that would be used. My mentors told me that the batteries kept shutting down, so it was my responsibility to remove excess fan grilles, rotate the fans, and then switch the aluminum standoffs with nylon ones so that the coolers could operate without problems. My last task is to collect all the battery test data and organize them into charts using Microsoft Excel, before the Branch is able to conduct its research on lithium ion batteries. Therefore, during my tenure, it is my responsibility to take down final test data

Reid, Robert O., II↗
NASA NTRS2006

Round Trip Energy Efficiency of NASA Glenn Regenerative Fuel Cell System

NASA Glenn Research Center (GRC) has recently demonstrated a Polymer Electrolyte Membrane (PEM) based hydrogen/oxygen regenerative fuel cell system (RFCS) that operated for a charge/discharge cycle with round trip efficiency (RTE) greater than 50 percent. The regenerative fuel cell system (RFCS) demonstrated closed loop energy storage over a pressure range of 90 to 190 psig. In charge mode, a constant electrical power profile of 7.1 kWe was absorbed by the RFCS and stored as pressurized hydrogen and oxygen gas. In discharge mode, the system delivered 3 to 4 kWe of electrical power along with product water. Fuel cell and electrolyzer power profiles and polarization performance are documented in this paper. Individual cell performance and the variation of cell voltages within the electrochemical stacks are also reported. Fuel cell efficiency, electrolyzer efficiency, and the system RTE were calculated from the test data and are included below.

Garcia, Christopher P.↗
NASA NTRS2013

Acceptability of Flight Deck-Based Interval Management Crew Procedures

The Interval Management for Near-term Operations Validation of Acceptability (IM-NOVA) experiment was conducted at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) in support of the NASA Next Generation Air Transportation System (NextGen) Airspace Systems Program's Air Traffic Management Technology Demonstration - 1 (ATD-1). ATD-1 is intended to showcase an integrated set of technologies that provide an efficient arrival solution for managing aircraft using NextGen surveillance, navigation, procedures, and automation for both airborne and ground-based systems. The goal of the IM-NOVA experiment was to assess if procedures outlined by the ATD-1 Concept of Operations, when used with a minimum set of Flight deck-based Interval Management (FIM) equipment and a prototype crew interface, were acceptable to and feasible for use by flight crews in a voice communications environment. To investigate an integrated arrival solution using ground-based air traffic control tools and aircraft automatic dependent surveillance broadcast (ADS-B) tools, the LaRC FIM system and the Traffic Management Advisor with Terminal Metering and Controller Managed Spacing tools developed at the NASA Ames Research Center (ARC) were integrated in LaRC's Air Traffic Operations Laboratory. Data were collected from 10 crews of current, qualified 757/767 pilots asked to fly a high-fidelity, fixed based simulator during scenarios conducted within an airspace environment modeled on the Dallas-Fort Worth (DFW) Terminal Radar Approach Control area. The aircraft simulator was equipped with the Airborne Spacing for Terminal Area Routes algorithm and a FIM crew interface consisting of electronic flight bags and ADS-B guidance displays. Researchers used "pseudo-pilot" stations to control 24 simulated aircraft that provided multiple air traffic flows into DFW, and recently retired DFW air traffic controllers served as confederate Center, Feeder, Final, and Tower controllers. Pilot participant feedback indicated that the procedures used by flight crews to receive and execute interval management (IM) clearances in a voice communications environment were logical, easy to follow, did not contain any missing or extraneous steps, and required the use of an acceptable level of workload. The majority of the pilot participants found the IM concept, in addition to the proposed FIM crew procedures, to be acceptable and indicated that the ATD-1 procedures can be successfully executed in a near-term NextGen environment.

Murdock, Jennifer L.↗
DOE OSTI2025

A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces

Interconnected landscape features such as terrestrial‐aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real‐time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions. A central design element is “one data logger program to rule them all”—a collection of sensor‐specific modules deployed on 40 loggers controlling ∼2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates ∼6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales.

Ward, Nicholas D. [Pacific Northwest National Labo↗
DOE OSTI2024

Kansas City, Missouri, Streetlight Electric Vehicle Charging: Strategies and challenges for site selection of streetlight electric vehicle infrastructure in Kansas City, Missouri (Final Report)

Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1989

Conceptual design for aerospace vehicles

The designers of aircraft and more recently, aerospace vehicles have always struggled with the problems of evolving their designs to produce a machine which would perform its assigned task(s) in some optimum fashion. Almost invariably this involved dealing with more variables and constraints than could be handled in any computationally feasible way. With the advent of the electronic digital computer, the possibilities for introducing more variable and constraints into the initial design process led to greater expectations for improvement in vehicle (system) efficiency. The creation of the large scale systems necessary to achieve optimum designs has, for many reason, proved to be difficult. From a technical standpoint, significant problems arise in the development of satisfactory algorithms for processing of data from the various technical disciplines in a way that would be compatible with the complex optimization function. Also, the creation of effective optimization routines for multi-variable and constraint situations which could lead to consistent results has lagged. The current capability for carrying out the conceptual design of an aircraft on an interdisciplinary bases was evaluated to determine the need for extending this capability, and if necessary, to recommend means by which this could be carried out. Based on a review of available documentation and individual consultations, it appears that there is extensive interest at Langley Research Center as well as in the aerospace community in providing a higher level of capability that meets the technical challenges. By implication, the current design capability is inadequate and it does not operate in a way that allows the various technical disciplines to participate and cooperately interact in the design process. Based on this assessment, it was concluded that substantial effort should be devoted to developing a computer-based conceptual design system that would provide the capability needed for the near-term as well as framework for development of more advanced methods to serve future needs.

Gratzer, Louis B.↗
NASA NTRSDate not supplied

Integrating Earth Observations and Socioeconomic Data to Address Health, Equity, and Environmental Justice

Access to reliable data about the characteristics of populations is a crucial component of decision making, policy development, and the assessment of progress towards strategic goals. Social determinants of health provide insight into the status of social and economic conditions within populations that have profound effects on public health, equity, and vulnerability. International frameworks such as the Sustainable Development Goals (SDGs) fundamentally focus on equality, but to efficiently address targets and indicators, socioeconomic data and Earth observations must be integrated to help identify populations most vulnerable to the impacts of climate change, natural disasters, health disparities, and poor policy planning. The ability to identify vulnerable populations with data analysis has the potential to empower community stakeholders with spatial awareness needed to inform decision-making that addresses equity and environmental justice issues within their communities. The EPA defines environmental justice (EJ) as “the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation and enforcement of environmental laws, regulations and policies.” NASA’s Earth Science Division (ESD) recognizes the benefits that Earth observations with NASA satellites create by equipping individuals with the knowledge to address community challenges. NASA’s Socioeconomic Data and Applications Center (SEDAC) supports the integration of socioeconomic and Earth science data as an “informational gateway.” This integration of data is advancing health, equity, and environmental justice initiatives.

Natasha Johnson-Griffin↗
DOE OSTI2024

Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing

This document summarizes the findings of a strategic planning exercise commissioned by the Weapons Simulation and Computing, Computational Physics (WSC/CP) program at the Lawrence Livermore National Laboratory (LLNL) in FY24. During the year, the committee met with multiple stakeholder communities to gather input, opinions, suggestions and concerns which have been incorporated throughout this document. The key findings from this exercise are summarized: • The development of multiphysics modelling and simulation (mod/sim) codes and software technologies, their deployment on exascale compute platforms, and their broad adoption across the NNSA is a major success of the Advanced Simulation and Computing (ASC) program and the Exascale Computing Project (ECP). Sustained investment in these core technologies is essential. • Today’s state of the art involves running ensembles of O(100K) simulations to perform uncertainty quantification (UQ) and design studies using multiple statistical methods such as Bayesian optimization to understand sensitivities of our models and explore parameterized design spaces. Even with exascale computing, we are practically limited to O(10) parameters in these studies since the number of simulations required to sample the space scales exponentially with the number of design parameters. • The data from these simulation ensembles is increasingly being used to train machine learned (ML) surrogates (or reduced order models, ROMs) which can then be used for optimization or real time design exploration. However, the trained surrogates are still limited in the number of parameters they can represent due to the sampling limitations previously noted. • Augmenting our suite of integrated multiphysics simulation codes, both current and emerging, with the ability to compute gradients (solution derivatives) of arbitrary simulation outputs with respect to (some or all) simulation inputs would be a breakthrough technology, opening the door to a new era of efficient and automated inverse design based on verified and validated mod/sim capabilities. • This capability, which we refer to as differentiable multiphysics codes (DMCs), would revolutionize both UQ and optimization studies by breaking the curse of dimensionality that presently limits our “gradient-free” ensemble based computing approach. A similar breakthrough occurred in the AI/ML community once the ability to compute gradients of arbitrary loss functions using back-propagation became commonplace. Gradient information from the multiphysics codes can also be used to dramatically improve the efficiency and scale of training of ML/ROM surrogates for rapid assessments. • Achieving this in our suite of codes will be a grand challenge, similar to the amount of effort that was required to transition from CPU to GPU computing. It will require buy-in from the entire WSC/CP program and beyond, including all integrated codes, physics and engineering models, third-party library dependencies and performance portability abstractions. It will also require investment in research and development of numerical methods for computing adjoints of coupled physics across multiple adaptively refined moving meshes and of stochastic (Monte Carlo) and mesh free (SPH) methods. • New software and numerical techniques, largely pioneered by the AI/ML community, make this feasible. Chief among these is automatic differentiation (AD), the ability to employ AD at point-wise locations in a physics calculation (instead of traditional black-box approaches) and the ability to perform “back-propagation in time” (or reverse mode AD) for non-linear partial differential equations (PDEs). Fundamentally, the conclusion of this strategic planning exercise is that the time is right to undertake a large scale effort in WSC, centered on the existing integrated codes, to continue the natural evolution of mod/sim in the age of AI/ML. Instead of attempting to replace mod/sim with purely data driven AI/ML models, we believe the key to success is to integrate AI/ML by building on top of the decades of hard-won knowledge and the verified/validated multiphysics modelling capability that is the hallmark of the ASC program.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Adaptation to high CO2 concentration in an optimal environment: radiation capture, canopy quantum yield and carbon use efficiency

The effect of elevated [CO2] on wheat (Triticum aestivum L. Veery 10) productivity was examined by analysing radiation capture, canopy quantum yield, canopy carbon use efficiency, harvest index and daily C gain. Canopies were grown at either 330 or 1200 micromoles mol-1 [CO2] in controlled environments, where root and shoot C fluxes were monitored continuously from emergence to harvest. A rapidly circulating hydroponic solution supplied nutrients, water and root zone oxygen. At harvest, dry mass predicted from gas exchange data was 102.8 +/- 4.7% of the observed dry mass in six trials. Neither radiation capture efficiency nor carbon use efficiency were affected by elevated [CO2], but yield increased by 13% due to a sustained increase in canopy quantum yield. CO2 enrichment increased root mass, tiller number and seed mass. Harvest index and chlorophyll concentration were unchanged, but CO2 enrichment increased average life cycle net photosynthesis (13%, P < 0.05) and root respiration (24%, P < 0.05). These data indicate that plant communities adapt to CO2 enrichment through changes in C allocation. Elevated [CO2] increases sink strength in optimal environments, resulting in sustained increases in photosynthetic capacity, canopy quantum yield and daily C gain throughout the life cycle.

Non-NASA Center↗
NASA NTRS2004

Advanced Controller for the Free-Piston Stirling Convertor

The free-piston Stirling power convertor is being considered as an advanced power conversion technology to be used for future NASA deep space missions requiring long life radioisotope power systems. This technology has a conversion efficiency of over 25%, which is significantly higher than the efficiency of the Radioisotope Thermal-electric Generators (RTG) now in use. The NASA Glenn Research Center has long been recognized as a leader in Stirling technology and is responsible for the development of advanced technologies that are intended to significantly improve key characteristics of the Stirling convertor. The advanced technologies identified for development also consider the requirements of potential future missions and the new capabilities that have become available in the associated technical areas. One of the key areas identified for technology development is the engine controller. To support this activity, an advanced controller is being developed for the Stirling power convertor. This controller utilizes active power factor correction electronics and microcontroller-based controls. The object of this paper is to present an overview of the advanced controller concept with modeling, simulation and hardware test data.

Gerber, Scott S.↗
NASA NTRS2002

Preliminary Sizing Completed for Single- Stage-To-Orbit Launch Vehicles Powered By Rocket-Based Combined Cycle Technology

Single-stage-to-orbit (SSTO) propulsion remains an elusive goal for launch vehicles. The physics of the problem is leading developers to a search for higher propulsion performance than is available with all-rocket power. Rocket-based combined cycle (RBCC) technology provides additional propulsion performance that may enable SSTO flight. Structural efficiency is also a major driving force in enabling SSTO flight. Increases in performance with RBCC propulsion are offset with the added size of the propulsion system. Geometrical considerations must be exploited to minimize the weight. Integration of the propulsion system with the vehicle must be carefully planned such that aeroperformance is not degraded and the air-breathing performance is enhanced. Consequently, the vehicle's structural architecture becomes one with the propulsion system architecture. Geometrical considerations applied to the integrated vehicle lead to low drag and high structural and volumetric efficiency. Sizing of the SSTO launch vehicle (GTX) is itself an elusive task. The weight of the vehicle depends strongly on the propellant required to meet the mission requirements. Changes in propellant requirements result in changes in the size of the vehicle, which in turn, affect the weight of the vehicle and change the propellant requirements. An iterative approach is necessary to size the vehicle to meet the flight requirements. GTX Sizer was developed to do exactly this. The governing geometry was built into a spreadsheet model along with scaling relationships. The scaling laws attempt to maintain structural integrity as the vehicle size is changed. Key aerodynamic relationships are maintained as the vehicle size is changed. The closed weight and center of gravity are displayed graphically on a plot of the synthesized vehicle. In addition, comprehensive tabular data of the subsystem weights and centers of gravity are generated. The model has been verified for accuracy with finite element analysis. The final trajectory was rerun using OTIS (Boeing Corporation's trajectory optimization software package), and the sizing output was incorporated into a solid model of the vehicle using PRO/Engineer computer-aided design software (Parametric Technology Corporation, Waltham, MA).

Roche, Joseph M.↗